2 citations · 3 across the 13 of their papers we have counts for
13 papers
VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
Jingxiang Sun, Chao Liao, Zhengxiong Luo +6
Current text-to-image systems typically employ a "text encoder plus diffusion decoder" paradigm, in which text semantics directly modulate continuous latent noise. Despite their su…
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
OmniGen-AR: AutoRegressive Any-to-Image Generation
Junke Wang, Xun Wang, Qiushan Guo +4
Autoregressive (AR) models have demonstrated strong potential in visual generation, offering superior performance with simple architectures and optimization objectives. However, ex…
Leveraging Verifier-Based Reinforcement Learning in Image Editing
Hanzhong Guo, Jie Wu, Jie Liu +6
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A k…
MEF: A Systematic Evaluation Framework for Text-to-Image Models
Xiaojing Dong, Weilin Huang, Liang Li +6
Rapid advances in text-to-image (T2I) generation have raised higher requirements for evaluation methodologies. Existing benchmarks center on objective capabilities and dimensions,…
Seedream 4.0: Toward Next-generation Multimodal Image Generation
Team Seedream, :, Yunpeng Chen +48
We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image compositi…
Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
Chao Liao, Liyang Liu, Xun Wang +7
Recent progress in unified models for image understanding and generation has been impressive, yet most approaches remain limited to single-modal generation conditioned on multiple…
Scaling Diffusion Transformers Efficiently via P
Chenyu Zheng, Xinyu Zhang, Rongzhen Wang +5
Diffusion Transformers have emerged as the foundation for vision generative models, but their scalability is limited by the high cost of hyperparameter (HP) tuning at large scales.…
SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL
Junke Wang, Zhi Tian, Xun Wang +4
This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications. Through careful exploration of training and inference o…
Seedream 3.0 Technical Report
Yu Gao, Lixue Gong, Qiushan Guo +28
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in…
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
Lixue Gong, Xiaoxia Hou, Fanshi Li +25
Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grappl…
Fast Prompt Alignment for Text-to-Image Generation
Khalil Mrini, Hanlin Lu, Linjie Yang +2
Text-to-image generation has advanced rapidly, yet aligning complex textual prompts with generated visuals remains challenging, especially with intricate object relationships and f…
SeedEdit: Align Image Re-Generation to Image Editing
Yichun Shi, Peng Wang, Weilin Huang
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance betwe…